MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets.

Abstract:

:A key challenge in analyzing metagenomics data pertains to assembly of sequenced DNA fragments (i.e. reads) originating from various microbes in a given environmental sample. Several existing methodologies can assemble reads originating from a single genome. However, these methodologies cannot be applied for efficient assembly of metagenomic sequence datasets. In this study, we present MetaCAA - a clustering-aided methodology which helps in improving the quality of metagenomic sequence assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly. A software implementation of MetaCAA is available at https://metagenomics.atc.tcs.com/MetaCAA.

journal_name

Genomics

journal_title

Genomics

authors

Reddy RM,Mohammed MH,Mande SS

doi

10.1016/j.ygeno.2014.02.007

subject

Has Abstract

pub_date

2014-02-01 00:00:00

pages

161-8

issue

2-3

eissn

0888-7543

issn

1089-8646

pii

S0888-7543(14)00013-5

journal_volume

103

pub_type

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